/*
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 *  This program is free software; you can redistribute it and/or modify
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 *  Additional permission under GNU GPL version 3 section 7:
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 *  KNIME interoperates with ECLIPSE solely via ECLIPSE's plug-in APIs.
 *  Hence, KNIME and ECLIPSE are both independent programs and are not
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 *  you the additional permission to use and propagate KNIME together with
 *  ECLIPSE with only the license terms in place for ECLIPSE applying to
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 *  Additional permission relating to nodes for KNIME that extend the Node
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 *  Nodes are deemed to be separate and independent programs and to not be
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 *  may freely choose the license terms applicable to such Node, including
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 *
 * History
 *   28.03.2017 (Adrian): created
 */
package org.knime.base.node.mine.regression.logistic.learner4.sg;

import java.util.Arrays;

import org.knime.base.node.mine.regression.logistic.learner4.data.TrainingData;
import org.knime.base.node.mine.regression.logistic.learner4.data.TrainingRow;
import org.knime.base.node.mine.regression.logistic.learner4.data.TrainingRow.FeatureIterator;

/**
 * Lazy implementation of stochastic gradient descent like optimization schemes.
 *
 * @author Adrian Nembach, KNIME.com
 */
final class LazySGOptimizer <T extends TrainingRow, U extends LazyUpdater<T>, R extends LazyRegularizationUpdater> extends AbstractSGOptimizer<T, U, R> {

    private int[] m_lastVisited;


    /**
     * Creates a LazySGOptimizer.
     * All arguments are delegated to the super class.
     *
     * @param data the training data to learn on
     * @param loss the loss to minimize
     * @param updaterFactory factory for a LazyUpdater
     * @param regularizationUpdater updater for regularization term
     * @param learningRateStrategy the strategy used for the learning rate for example fixed
     * @param stoppingCriterion determines when to stop the training
     * @param calcCovMatrix flag that indicates whether the covariance matrix of the coefficients should be calculated
     */
    public LazySGOptimizer(final TrainingData<T> data, final Loss<T> loss, final UpdaterFactory<T, U> updaterFactory,
        final R regularizationUpdater, final LearningRateStrategy<T> learningRateStrategy,
        final StoppingCriterion<T> stoppingCriterion, final boolean calcCovMatrix) {
        super(data, loss, updaterFactory, regularizationUpdater, learningRateStrategy, stoppingCriterion, calcCovMatrix);
        m_lastVisited = new int[data.getFeatureCount()];
    }



    /**
     * {@inheritDoc}
     */
    @Override
    protected void performUpdate(final T x, final U updater, final double[] gradient,
        final WeightMatrix<T> beta, final double stepSize, final int iteration) {
        updater.update(x, gradient, beta, stepSize, iteration);
    }


    /**
     * {@inheritDoc}
     */
    @Override
    protected void prepareIteration(final WeightMatrix<T> beta, final T x, final U updater, final R regUpdater,
        final int iteration) {
        // apply lazy updates
        updater.lazyUpdate(beta, x, m_lastVisited, iteration);
        regUpdater.lazyUpdate(beta, x, m_lastVisited, iteration);
        // update when present features were last visited
        for (FeatureIterator iter = x.getFeatureIterator(); iter.next();) {
            m_lastVisited[iter.getFeatureIndex()] = iteration;
        }
    }



    /**
     * {@inheritDoc}
     */
    @Override
    protected void postProcessEpoch(final WeightMatrix<T> beta, final U updater, final R regUpdater) {
        updater.resetJITSystem(beta, m_lastVisited);
        regUpdater.resetJITSystem(beta, m_lastVisited);
        Arrays.fill(m_lastVisited, 0);
    }



    /**
     * {@inheritDoc}
     */
    @Override
    protected void normalize(final WeightMatrix<T> beta, final U updater, final int iteration) {
        updater.normalize(beta, m_lastVisited, iteration);
        for (int i = 0; i < m_lastVisited.length; i++) {
            m_lastVisited[i] = iteration + 1;
        }
    }

}
